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Seminars on Stata in Washington, D.C.: Abstracts

April 8, 2010

Easy automation and reproducible analysis

Bill Rising
Director of Educational Services
Learn how to use both script files and the Stata GUI (menus, dialog boxes, Variables Manager, Data Editor, and Do-file Editor) to perform reproducible analyses with both result and command logging.

Panel/longitudinal data and multilevel mixed-effects modeling

Roberto G. Gutierrez
Director of Statistics
We will briefly cover the wide range of commands in Stata for estimating models of continuous, count, and binary outcomes with fixed effects and random effects. We will then extend random-effects estimation to intercepts and coefficients at multiple levels. These multilevel models are estimated by xtmixed for continuous outcomes, xtmelogit for binary outcomes, and xtmepoisson for count outcomes. All three commands share a similar syntax for model specification and for postestimation analysis.

Survey data

Roberto G. Gutierrez
Director of Statistics
Most of Stata’s estimation commands are equipped to automatically handle data from complex surveys. So long as we declare the survey aspects of our data, the estimates and their standard errors are adjusted for pre- and poststratification, multilevel sampling (clustering), and weighted sampling. We will cover declaring survey data and estimation, as well as the three primary survey variance estimators: linearization, balanced repeated replication, and jackknife.

Multiple imputation for missing data

Roberto G. Gutierrez
Director of Statistics
Multiple imputation provides a unified framework for handling missing data that is missing at random (MAR) or missing completely at random (MCAR). We will introduce Stata’s suite of mi commands for imputation, estimation, and data management.

Special topics

Bill Rising
Director of Educational Services
We will cover a number of topics: 1) how the division of estimation and postestimation (estimates, tests and confidence intervals of linear and nonlinear combinations, marginal effects, linear and nonlinear predictions, etc.) provides a common and powerful framework for performing analyses, 2) Stata’s extensibility and its relation to the active Stata user community, and 3) graphics, graphics editing, and creating custom graph profiles. We will also briefly discuss how what we have learned earlier applies to other estimation areas, such as survival analysis, univariate and multivariate time series, and multivariate methods.
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